23 citations · 42 across the 10 of their papers we have counts for
10 papers
PromptEmbedder: Efficient and Transferable Text Embedding via Dual-LLM Soft Prompting
Yu-Che Tsai, Kuan-Yu Chen, Yuan-Hao Chen +4
Large Language Models (LLMs) have demonstrated remarkable efficacy in text embedding, yet current adaptation methods like LoRA face significant bottlenecks in computational efficie…
Decision-aware User Simulation Agent for Evaluating Conversational Recommender Systems
Yuan-Chi Li, Li-Chi Chen, Sung-Yi Wu +2
Conversational recommender systems (CRS) increasingly rely on user simulators for automated evaluation of sales agents. A key requirement for such simulators is the ability to mode…
Concept-Aware Privacy Mechanisms for Defending Embedding Inversion Attacks
Yu-Che Tsai, Hsiang Hsiao, Kuan-Yu Chen +1
Text embeddings enable numerous NLP applications but face severe privacy risks from embedding inversion attacks, which can expose sensitive attributes or reconstruct raw text. Exis…
Let LLMs Speak Embedding Languages: Generative Text Embeddings via Iterative Contrastive Refinement
Yu-Che Tsai, Kuan-Yu Chen, Yuan-Chi Li +3
Existing large language model (LLM)-based embeddings typically adopt an encoder-only paradigm, treating LLMs as static feature extractors and overlooking their core generative stre…
Transferable Embedding Inversion Attack: Uncovering Privacy Risks in Text Embeddings without Model Queries
Yu-Hsiang Huang, Yuche Tsai, Hsiang Hsiao +2
This study investigates the privacy risks associated with text embeddings, focusing on the scenario where attackers cannot access the original embedding model. Contrary to previous…
Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions
Cheng-Te Li, Yu-Che Tsai, Chih-Yao Chen +1
In this survey, we dive into Tabular Data Learning (TDL) using Graph Neural Networks (GNNs), a domain where deep learning-based approaches have increasingly shown superior performa…
AutoML-GPT: Large Language Model for AutoML
Yun-Da Tsai, Yu-Che Tsai, Bo-Wei Huang +2
With the emerging trend of GPT models, we have established a framework called AutoML-GPT that integrates a comprehensive set of tools and libraries. This framework grants users acc…
Social network modeling and applications, a tutorial
Lisette Espín-Noboa, Tiago Peixoto, Fariba Karimi
Social networks have been widely studied over the last century from multiple disciplines to understand societal issues such as inequality in employment rates, managerial performanc…
GraphFC: Customs Fraud Detection with Label Scarcity
Karandeep Singh, Yu-Che Tsai, Cheng-Te Li +2
Custom officials across the world encounter huge volumes of transactions. With increased connectivity and globalization, the customs transactions continue to grow every year. Assoc…
FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks
Yi-Ling Hsu, Yu-Che Tsai, Cheng-Te Li
Financial technology (FinTech) has drawn much attention among investors and companies. While conventional stock analysis in FinTech targets at predicting stock prices, less effort…